DataFlux

How MDM with Data Quality Can Better Enable Business Intelligence

By Daniel Teachey, Senior Director of Marketing, DataFlux

While data quality has always been an important element of many IT functions like analytics, master data management, spend analysis and business intelligence, its role as a catalyst for optimizing the value of enterprise information has never before been so essential to the recovery and maintenance of business operations everywhere. The financial crisis has led to widespread upheaval for many companies, with mergers and acquisitions, layoffs, loss of clients, address changes and budget cutbacks compounding the problem of dirty data unlike anything previously imagined.

MDM, like cloud computing and SOA, may seem like a “luxury” initiative that isn’t necessary for companies to survive in the short-term during this recession. However, it can offer serious ROI and competitive advantage in a time where businesses cannot afford to be relegated to the shadows. Part of this competitive advantage afforded by MDM is knowing exactly who your customers are, what they’re buying, when they’re buying it and what their behavior patterns have demonstrated in the past. Plus, by avoiding the costly and oftentimes ineffective process of cleansing and de-duplicating data after it has been migrated into a repository (the equivalent of mopping up thousands of dirty shoeprints instead of having a doormat at the entrance), companies can save considerable money.

MDM provides the ability to see customer patterns visualized graphically, textually, geometrically, all available in real-time for analysts, sales representatives and the C-suite. The ability to see these patterns in one place without having to wait for the IT division to print out a series of complicated, instantly-outdated reports improves the way an organization runs. In other words, it provides business intelligence. Once again, like everything else in the IT industry, MDM will prove futile and wasteful without ensuring sound data quality at the start.

To be sure, given the limited availability of disposable funds and outside investment, now is the time to make sure that the planning behind an MDM program is flawless. One thing is certainly for sure; if a company’s data quality isn’t in top shape with solid data governance in place then MDM will not succeed. A common mistake is for companies to think that MDM is a quick-fix for all one’s data ills, an “in-a-box” solution for poor data quality practices. Far from it, as sloppily-executed MDM deployments can hemorrhage money and waste precious work time. If you’re ready to deploy MDM the right way, here are some things to keep in mind.

First, remember that data quality readies data records for entry into a final MDM repository, not the other way around. MDM doesn’t enable data quality, but it does better enable BI because it provides carefully screened master records that present a single view of the customer – a single version of the truth. Rather than assessing the buying patterns of John Smith, J.R. Smith and Jonathan Smith, who ironically all share the same address and all bought the same products at the same time; an MDM repository provides an integrated, de-duplicated, federated single entry for the one and only Jonathan Robert Smith. This record will then be used by a BI solution to produce a valuable, clean and fully accurate report (with pie charts and grid views or whatever format is requested). Positive ROI from BI initiatives is the result of a well-executed MDM program incorporating the best-quality data – data reconciled from various sources, cleansed, standardized and de-duplicated for integration into a single master repository.

It is easy to see why MDM enables BI better than the traditional scenario, which is to migrate data, structured or unstructured from independent silos directly into a BI tool that then spits out reports that at most would provide only a general view of trends. After all, these silos are often overseen by different groups, possibly with different formats or no format at all. BI is a real-time vehicle to deliver actionable insights to departments within an enterprise, and for the purposes of analogy, let’s imagine it as just that – a real vehicle.

A business intelligence solution/software tool is the luxury sedan of customer insights and holistic views of business performance; data is gasoline; and MDM is a super high-octane fuel pump. A company can put regular, poorly refined fuel into the tank and the high-performance car will have a bumpy and rumbling ride from point A to B. If too much of this fuel is added over a period of time, it will damage the car’s engine and frequent trips to the service department to cleanse and repair the engine will be required. The overall life of the car will be reduced and the shiny new car will drive like a 1985 economy hatchback.

If 93-octane fuel is delivered via the special pump, which the luxury sedan was created to consume, the car will run as it was meant to, on all cylinders with a smooth ride and proper gas mileage and fewer emissions. The engine will stay clean so long as 93-octane is used and the car will require few check-ups and repairs. Ultimately, although it might seem like a quick-fix or cheap alternative just to select the less-expensive 87-octane gasoline, it costs more in the end in terms of productivity, driving comfort, mileage and constant repairs. While the 93-octane gas requires more effort and refining initially, it will offer superior low-maintenance performance, and most importantly, yield the highest return-on-investment for the price and design of the luxury car.

Companies ought to heed this analogy when tacitly ignoring the quality of their data, lest one imagine what would happen if they haphazardly selected Diesel?